Fundamenta Informaticae 83 (2008) 231–252 231 IOS Press Non-dominated Rank based Sorting Genetic Algorithms Ashish Ghosh Machine Intelligence Unit and Center for Soft Computing Research Indian Statistical Institute 203 B. T. Road, Kolkata 700108, India ash@isical.ac.in Mrinal Kanti Das Indian Institute of Science Bangalore, 560 012, India nmrinl@csa.iisc.ernet.in Abstract. In this paper a new concept of ranking among the solutions of the same front, along with elite preservation mechanism and ensuring diversity through the nearest neighbor method is proposed for multi-objective genetic algorithms. This algorithm is applied on a set of benchmark multi-objective test problems and the results are compared with that of NSGA-II (a similar algo- rithm). The proposed algorithm is seen to over perform the existing algorithm. More specifically, the new approach has been used to solve the deceptive multi-objective optimization problems in a better way. Keywords: Multi-objective optimization, evolutionary computing, genetic algorithms, Pareto op- timality 1. Introduction Genetic Algorithm (GA) developed by Holland (1960) is a model of machine learning, that derives its behavior from a metaphor of the processes of natural evolution and natural genetics. Evolution, in essence, is a two-step process of random variation and selection. It can be modeled mathematically as: X[t + 1] = s(v(X[t])) Address for correspondence: Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700 108, India, ash@isical.ac.in